Abstract
In this work a hybrid simulation-optimization approach is presented to support decision-making towards improved resiliency and sustainability in pharmaceutical supply chain (PSC) operations. In a first step, a simulation model is used to assess the PSC performance under a set of disruptive scenarios to select the best inventory-based strategy for enhanced resiliency. Disruptions addressed in this work are mainly related to unpredicted medium-term production stoppages due to unexpected high-impact events such as accidents in production and transportation, or natural disasters. In a second step, a multi-objective mixed integer linear programming (MO-MILP) model is developed to optimize the selected inventory-based strategy regarding the economic, social, and environmental dimensions. In particular, the social and environmental aspects are introduced by anticipating the expected waste generation of close to expire medicines, redirecting them into a donation scheme. The proposed approach is applied to a representative PSC, with preliminary results showing the relevance of this tool for decision-makers to assess the trade-offs associated to the economic and social dimensions, as well as their impacts on waste generation.

Abstract
Warehouses and distribution centres play a key role in any Supply Chain, particularly in the retail sector, where a network of stores needs to be replenished in a highly dynamic and increasingly uncertain context. In this regard, companies need to improve their intralogistics systems daily to ensure long-term competitiveness and sustainable growth. This is especially true in picking-by-line systems where many time-consuming and manual tasks are usually involved. This study introduces a new decision support tool based on simulation methods to aid the decision-making process in a picking-by-Line system, aimed to improve the overall picking operations efficiency, through human-centric perspective. A Discrete-Event-Simulation model is proposed to assess a set of parameters under several scenarios, driving a more informed decision-making process towards cost-effective strategies. The proposed approach was validated through an empirical case study showing its effectiveness in assisting operational planning decisions related to capacity and resource allocation. The system demonstrates promising versatility for application across varied warehouse environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
This work extends the energy-efficient job shop scheduling problem with transport resources by considering speed adjustable resources of two types, namely: the machines where the jobs are processed on and the vehicles that transport the jobs around the shop-floor. Therefore, the problem being considered involves determining, simultaneously, the processing speed of each production operation, the sequence of the production operations for each machine, the allocation of the transport tasks to vehicles, the travelling speed of each task for the empty and for the loaded legs, and the sequence of the transport tasks for each vehicle. Among the possible solutions, we are interested in those providing trade-offs between makespan and total energy consumption (Pareto solutions). To that end, we develop and solve a bi-objective mixed-integer linear programming model. In addition, due to problem complexity we also propose a multi-objective biased random key genetic algorithm that simultaneously evolves several populations. The computational experiments performed have show it to be effective and efficient, even in the presence of larger problem instances. Finally, we provide extensive time and energy trade-off analysis (Pareto front) to infer the advantages of considering speed adjustable machines and speed adjustable vehicles and provide general insights for the managers dealing with such a complex problem.

Abstract
The circular economy is increasingly being considered as a potential model to replace the prevailing end-of-life approach by establishing a closed-loop flow. The importance of different supply chain (SC) actors in this process has been recognized as a critical aspect of the development of sustainable production-consumption models. Consumers play a crucial role in this context, as they have a dual function: ensuring the correct disposal of used products; and consuming products from circular sources. However, the different roles consumers play (refuse, rethink, reduce, reuse, repair, refurbish, remanufacture, repurpose, recycle, recover) in circular SCs are still unclear. Through a systematic literature review, this paper aims to contribute to a better understanding of the influence of consumers on the adoption of circular supply chain (CSC) practices and to identify the main drivers and barriers regarding the adoption of circular practices. The results demonstrate that the topic is recent and has gained ground in the literature. An in-depth qualitative analysis was carried out with the 74 papers identified and shows that the most commonly addressed R-strategies are reuse, recycle and repair. The main motivations and challenges towards a greater adoption of circular practices are related to (or lack of) environmental beliefs and financial benefits.

Abstract
This paper analyses how the harmonization between supply and demand uncertainty and supply chain responsiveness (SC fit) impacts business performance. The study analyses data obtained from a sample of 179 manufacturing companies from Portugal. The business performance of companies with different types of SC fit (high-high fit and low-low fit) and misfit (positive and negative) were analyzed and discussed. The results indicate that SC fit is positively related to business performance, economic and productivity, and commercial performance separately. This study advances the literature as the results indicate that SC fit positively affects both commercial and economic, and productivity performance. In contrast, previous empirical studies have mainly addressed the impact only on financial and operational performance.

Abstract
Managing supply chain (SC) relationships to deal with challenges posed by contemporary social and business environments is a difficult task that can be facilitated with the use of digital technologies. The growing complexity of supply chains, characterized by over-dependencies on geographically dispersed partners across different regions, increases risks related to managing these relationships and highlights the importance of collaboration and balancing the power dynamics between SC partners. Previous studies have shown that small and medium enterprises (SMEs) can be considered the weakest link in terms of digitization and balance of power. This article aims to analyse how buyer-seller power relations moderate the relationship between the adoption of digital technologies in supply chain management (SCM) processes and innovation performance in the context of SMEs. Data were collected from manufacturing SMEs operating in Portugal. The results support the assumption that the use of digital technologies in processes related to SCM has a positive effect on SMEs innovation performance. The results also suggest that non-mediated power and reward-mediated positively moderate the relationship between the adoption of digital technologies and innovation performance, while the impact of coercive-mediated power was not confirmed. The article contributes to theory and practice by advancing the literature and guiding managers in the challenging task of carrying out digital transformation initiatives, considering their relationship with the power dynamics in the complex context of SMEs.